'DataLoader' object does not support indexing

Question

I have downloaded the ImageNet dataset via this pytorch api by setting download=True. But I cannot iterate through the dataloader.

The error says “‘DataLoader’ object does not support indexing”

trainset = torch.utils.data.DataLoader(
    datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', split='train',
                      download=False))
trainloader = torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False, num_workers=1)

I tried a simple approach I just tried to run the following,

trainloader[0]

In the root directory, the pattern is

root/  
    train/  
          n01440764/
          n01443537/ 
                   n01443537_2.jpg

The docs in the official website doesnt say anything else. https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet

What am I doing wrong ?

Asked By: Farshid Rayhan

||

Source

Answer 1

The input dataset to torch.utils.data.DataLoader() should be of type torch.utils.data.Dataset, not torch.utils.data.DataLoader, which is what you are doing in above code.

So, your above code should be:

trainset = torchvision.datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', 
                                          split='train', 
                                          download=False)

trainloader = torch.utils.data.DataLoader(trainset, 
                                          batch_size=1, 
                                          shuffle=False, 
                                          num_workers=1)

For more details, check the official torch documentation here.

Answered By: Anubhav Singh

Answer 2

Well, the answer is pretty simple (besides error mentioned in the other answer).

DataLoader has no __getitem__ method (see in the source code for yourself).

It is used for iterating, not random access, over data (or batches of data). If you want to access specific element you should use torch.utils.data.Dataset, in your case:

trainset = torchvision.datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', split='train', )
trainset[0]

Getting a batch

If you want to get a batch you may iterate over it and break afterwards:

for batch in dataloader:
    print(batch) # or anything else you want to do
    break

DataLoader creates random indices in default or specified way (see samplers), hence there is no __getitem__ as it wouldn’t make sense for this object.

You may also inherit from the DataLoader and create your own __getitem__ function doing what you want (more complicated though).

Full example

# torch.utils.data.Dataset object
trainset = datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/', split='train', download=True)
# torch.utils.data.DataLoader object
trainloader =torch.utils.data.DataLoader(trainset, batch_size=1, shuffle=False)

for batch in trainloader:
    print(batch)
    break

Above should print the first batch whatever is inside.

Answered By: Szymon Maszke

Answer 3

Solution

input_transform = standard_transforms.Compose([
    transforms.Resize((255,255)), # to Make sure all the 
    transforms.CenterCrop(224),   # imgs are at the same size 
    transforms.ToTensor()
])  


# torch.utils.data.Dataset object
trainset = datasets.ImageNet('/media/farshid/DataStore/temp/Imagenet/',
                             split='train', download=False, transform = input_transform)
# torch.utils.data.DataLoader object
trainloader =torch.utils.data.DataLoader(trainset, batch_size=2, shuffle=False)


for batch_idx, data in enumerate(trainloader, 0):
    x, y = data 
    break

Answered By: Farshid Rayhan

Answer 4

I ended up with this dirty solution:

def Dataloader_by_Index(data_loader, target=0):
    for index, data in enumerate(data_loader):
        if index == target:
            return data
    return None
fifth_element = Dataloader_by_Index(my_data_loader, target=4)

Answered By: Sakib Ahmed Sumdany

'DataLoader' object does not support indexing

Question:

Answers:

Getting a batch

Full example